Transformer models are also increasingly the baseline architecture used for code generation tasks, such as writing methods from natural language description [1]}, [2]}, [3]}, or generating test cases from the focal method under test [4]}. Similarly for NLP tasks these models are pre-trained on a large corpus of natural text and publicly available source code and then fine-tuned on a specific code-related task. Further, these models also may not generalize to new domains of interest, and can benefit from task or even user-specific fine-tuning, here called customization or personalization. Customization is particularly relevant for code generation models since it provides several benefits:
